Type hint for python version lower than 3.9

3 files changed, 18 insertions, 18 deletions

diff --git a/models/model.py b/models/model.py
index 4deced0..725988a 100644
--- a/models/model.py
+++ b/models/model.py
@@ -1,5 +1,5 @@
 import os
-from typing import Union, Optional, Tuple, List
+from typing import Union, Optional, Tuple, List, Dict
 
 import numpy as np
 import torch
@@ -166,7 +166,7 @@ class Model:
             popped_keys=['root_dir', 'cache_on']
         )
         self.log_name = '_'.join((self.log_name, self._dataset_sig))
-        config: dict = dataset_config.copy()
+        config: Dict = dataset_config.copy()
         name = config.pop('name')
         if name == 'CASIA-B':
             return CASIAB(**config, is_train=self.is_train)
@@ -180,7 +180,7 @@ class Model:
             dataset: Union[CASIAB],
             dataloader_config: DataloaderConfiguration
     ) -> DataLoader:
-        config: dict = dataloader_config.copy()
+        config: Dict = dataloader_config.copy()
         if self.is_train:
             (self.pr, self.k) = config.pop('batch_size')
             self.log_name = '_'.join((self.log_name, str(self.pr), str(self.k)))
@@ -195,9 +195,9 @@ class Model:
 
     def _batch_splitter(
             self,
-            batch: List[dict[str, Union[np.int64, str, torch.Tensor]]]
-    ) -> Tuple[dict[str, Union[List[str], torch.Tensor]],
-               dict[str, Union[List[str], torch.Tensor]]]:
+            batch: List[Dict[str, Union[np.int64, str, torch.Tensor]]]
+    ) -> Tuple[Dict[str, Union[List[str], torch.Tensor]],
+               Dict[str, Union[List[str], torch.Tensor]]]:
         """
         Disentanglement need two random conditions, this function will
         split pr * k * 2 samples to 2 dicts each containing pr * k
@@ -211,7 +211,7 @@ class Model:
         return default_collate(_batch[0]), default_collate(_batch[1])
 
     def _make_signature(self,
-                        config: dict,
+                        config: Dict,
                         popped_keys: Optional[List] = None) -> str:
         _config = config.copy()
         if popped_keys:
diff --git a/utils/configuration.py b/utils/configuration.py
index aa04b32..455abe8 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -1,4 +1,4 @@
-from typing import TypedDict, Optional, Union, Tuple
+from typing import TypedDict, Optional, Union, Tuple, Dict
 
 from utils.dataset import ClipClasses, ClipConditions, ClipViews
 
@@ -15,7 +15,7 @@ class DatasetConfiguration(TypedDict):
     train_size: int
     num_sampled_frames: int
     discard_threshold: int
-    selector: Optional[dict[str, Union[ClipClasses, ClipConditions, ClipViews]]]
+    selector: Optional[Dict[str, Union[ClipClasses, ClipConditions, ClipViews]]]
     num_input_channels: int
     frame_size: Tuple[int, int]
     cache_on: bool
diff --git a/utils/dataset.py b/utils/dataset.py
index 0a33693..e691157 100644
--- a/utils/dataset.py
+++ b/utils/dataset.py
@@ -1,7 +1,7 @@
 import os
 import random
 import re
-from typing import Optional, NewType, Union, List, Tuple
+from typing import Optional, NewType, Union, List, Tuple, Set, Dict
 
 import numpy as np
 import torch
@@ -11,9 +11,9 @@ from sklearn.preprocessing import LabelEncoder
 from torch.utils import data
 from tqdm import tqdm
 
-ClipClasses = NewType('ClipClasses', set[str])
-ClipConditions = NewType('ClipConditions', set[str])
-ClipViews = NewType('ClipViews', set[str])
+ClipClasses = NewType('ClipClasses', Set[str])
+ClipConditions = NewType('ClipConditions', Set[str])
+ClipViews = NewType('ClipViews', Set[str])
 
 
 class CASIAB(data.Dataset):
@@ -26,7 +26,7 @@ class CASIAB(data.Dataset):
             train_size: int = 74,
             num_sampled_frames: int = 30,
             discard_threshold: int = 15,
-            selector: Optional[dict[
+            selector: Optional[Dict[
                 str, Union[ClipClasses, ClipConditions, ClipViews]
             ]] = None,
             num_input_channels: int = 3,
@@ -75,12 +75,12 @@ class CASIAB(data.Dataset):
         self.views: np.ndarray[np.str_]
         # Labels, classes, conditions and views in dataset,
         #   set of three attributes above
-        self.metadata = dict[str, List[np.int64, str]]
+        self.metadata = Dict[str, List[np.int64, str]]
 
         # Dictionaries for indexing frames and frame names by clip name
         # and chip path when cache is on
-        self._cached_clips_frame_names: Optional[dict[str, List[str]]] = None
-        self._cached_clips: Optional[dict[str, torch.Tensor]] = None
+        self._cached_clips_frame_names: Optional[Dict[str, List[str]]] = None
+        self._cached_clips: Optional[Dict[str, torch.Tensor]] = None
 
         # Video clip directory names
         self._clip_names: List[str] = []
@@ -170,7 +170,7 @@ class CASIAB(data.Dataset):
     def __getitem__(
             self,
             index: int
-    ) -> dict[str, Union[np.int64, str, torch.Tensor]]:
+    ) -> Dict[str, Union[np.int64, str, torch.Tensor]]:
         label = self.labels[index]
         condition = self.conditions[index]
         view = self.views[index]